%{
AUTHOR: Felipe Arteaga
-------------------------------------------------------------------------
PROJECT: Warnings
-------------------------------------------------------------------------
DESCRIPTION:
=========================================================================
%}


clearvars -except projectDir projectDirData fromMainWarningsPaper
clc;close all;fclose('all');feature('DefaultCharacterSet','UTF-8');

if(not(exist('projectDir','var')==1&&exist('projectDirData','var')==1&&exist('fromMainWarningsPaper','var')==1&&fromMainWarningsPaper))
    pcName=char(java.lang.System.getProperty('user.name'));
    if(strcmp(pcName,'felipe'))
        % PC Felipe
        myDir='/Users/felipe/Dropbox/';
        projectDir=[myDir,'git/warnings/'];
        projectDirData=[myDir,'projects/warnings/'];
        addpath(genpath([myDir,'/myMatlabFunctions/']));
    end
end
compileLatexTable=false;

%%
dirPlots=[projectDir,'/paper/figuresCL/RDs/'];
dirTable=[projectDir,'/paper/tablesCL/'];

warning('Alternative dir plots')
dirPlots=[projectDir,'/paper/figuresCL/RDs_aux/'];
dirPlotsR=[projectDir,'/paper/figuresCL/RDs_multBandwidth/'];

dirData=[projectDirData,'/data/chile/'];


plotRDs=true;
plotQuantiles=false;
quantiles=[.25,.75];
titlePlotRD=false;
plotWithZoom=false;
savePlotRDs=true;
bandwidthTable='local'; % 'full','local','localAuto';

tableBandwidthComparison=true;
posBandwidth=1; % This is for the bandwidht robust excercise. Indicates the subpopulation that we are using the bandwidth table
plotAndSaveRobust=true; % Plots of robust bandwidht RD
% For making subfigures with plots:
maxHorzPlots=3;
maxVertPlots=4;

switch bandwidthTable
    case 'full'
        bwnote='Full bandwidth was used (-0.28,+0.68) with 2nd order (local) polynomial fit. ';
    case 'local'
        bwnote='Bandwidth was set to +-0.1 with default local polynomial fit (1st order). ';
    case 'localAuto'
        bwnote='Bandwidth was set to automatic (bwselect with default options) with default polynomial fit (1st order). ';
end

%% Load data:
anho=2020; %0: pooled
if(anho==0)
    anhoStr='';
else
    anhoStr=sprintf('%i',anho);
end


notIn2020={}; % {'assignedToPref','acceptOffer','declineOffer','preferenciaAsign_1','placedInAnyPreferenceIniEnd','placedInAddedIniEnd','placedInAddedNoWaitlistIniEnd','placedInOldIniEnd','enrolledInAssigned','enrolledInAddedIniEnd','enrolledInAddedNoWaitlistIniEnd','enrolledInAddedNoRiskIniEnd'};

if(anho==0)
    
    d1=load([dirData,'2018/inputRD'],'dataRD');
    d2=load([dirData,'2019/inputRD'],'dataRD');
    d3=load([dirData,'2020/inputRD'],'dataRD');
    % Variables that are not available for 2020 yet:
    fillWithNan=notIn2020;
    %fillWithNan=
    for v=1:length(fillWithNan)
        d3.dataRD.(fillWithNan{v})=nan(height(d3.dataRD),1);
    end
    
    
    % Universe that is comparable between 2018-2020
    entryGrades=[-1 0 1 7 9];
    sirven1=ismember(d1.dataRD.grade,entryGrades)&not(d1.dataRD.cod_reg==13);
    sirven2=ismember(d2.dataRD.grade,entryGrades)&not(d2.dataRD.cod_reg==13);
    sirven3=ismember(d3.dataRD.grade,entryGrades)&not(d3.dataRD.cod_reg==13);
    
    d1.dataRD.comparable=sirven1;
    d2.dataRD.comparable=sirven2;
    d3.dataRD.comparable=sirven3;
    
    d1.dataRD.anho=2018*ones(height(d1.dataRD),1);
    d2.dataRD.anho=2019*ones(height(d2.dataRD),1);
    d3.dataRD.anho=2020*ones(height(d3.dataRD),1);
    
    % Keep vars in common
    
    incommon=intersect(intersect(d1.dataRD.Properties.VariableNames,d2.dataRD.Properties.VariableNames),d3.dataRD.Properties.VariableNames);
    incommon=incommon(not(strcmp(incommon,'mrun')));
    dataRD=[d1.dataRD(:,incommon);d2.dataRD(:,incommon);d3.dataRD(:,incommon)];
    
    
    
else
    load([dirData,anhoStr,'/inputRD'],'dataRD')
    dataRD.anho=scalarForTable(anho,dataRD);
end

dataRD.rural=dataRD.newMarket<0;

% As asignment at .3 is control, just move (if any) in .3 to epsilon to the
% left:
dataRD.riskPopup(dataRD.riskPopup==.3)=.29999999;

% Avoid mass points at extremes:
dataRD.restrAll=dataRD.pobPopup==1&dataRD.riskPopup>.01&dataRD.riskPopup<.99;

% No need to be more specific, and put "Predicted placement risk of 1st
% attempt"
dataRD.Properties.VariableDescriptions{'riskPopup'}='Predicted placement risk';
dataRD.Properties.VariableDescriptions{'treatedPopup'}='Treated pop-up in first attempt';


if(false)
    %% % Popups of total population
    n_apps=[274990,483070,454415];
    
    fprintf('2018 In data: %.1f, in popup population: %.1f\n',height(d1.dataRD)/n_apps(1)*100,sum(d1.dataRD.pobPopup)/n_apps(1)*100);
    fprintf('2019 In data: %.1f, in popup population: %.1f\n',height(d2.dataRD)/n_apps(2)*100,sum(d2.dataRD.pobPopup)/n_apps(2)*100);
    fprintf('2020 In data: %.1f, in popup population: %.1f\n',height(d3.dataRD)/n_apps(3)*100,sum(d3.dataRD.pobPopup)/n_apps(3)*100);
    
    fprintf('\n ALL  In data: %.1f, in popup population: %.1f\n',height(dataRD)/sum(n_apps)*100,sum(dataRD.pobPopup)/sum(n_apps)*100);
    
end





%% RD

% Defines subpopulation in which I want to calculate RDs for outcomes
% defined soon.

if(anho==0) % This calculates estimates pooling all years
    
    
    subpobs=cell(1,4);
    s=1;
    %subpobs(s,:)={'All',    dataRD.restrAll,'pooled','addAnyIniEnd'};s=s+1;
    %subpobs(s,:)={'2018',   dataRD.restrAll&dataRD.anho==2018,'2018',''};s=s+1;
    %subpobs(s,:)={'2019',      dataRD.restrAll&dataRD.anho==2019,'2019',''};s=s+1;
    subpobs(s,:)={'2020',    dataRD.restrAll&dataRD.anho==2020,'2020',''};s=s+1;
    posAll=1;
    
    
    
    
    
    
else
    subpobs=cell(1,4);
    s=1;
    %subpobs(s,:)={'All',    dataRD.restrAll,'pooled','addAnyIniEnd'};s=s+1;
    %subpobs(s,:)={'2018',   dataRD.restrAll&dataRD.anho==2018,'2018',''};s=s+1;
    %subpobs(s,:)={'2019',      dataRD.restrAll&dataRD.anho==2019,'2019',''};s=s+1;
    subpobs(s,:)={'2020',    dataRD.restrAll&dataRD.anho==2020,'2020','addAnyIniEndSurvey'};s=s+1;
    posAll=1;
end
dataRD.orden=(1:height(dataRD))';

% Definition of outcomes for RDs. 3rd row defines the beginning of a panel
% in the table
% 4th row

if(ismember('DRiskLExpostIniEnd',dataRD.Properties.VariableNames))
    error('Borrar esto');
else
    dataRD.DRiskExpostIniEnd=dataRD.riskExpostEnd-dataRD.riskExpostIni;
end

% New: enrolled conditional on placed (short name because of stata)
dataRD.enrolledInAssignedCOA=double(dataRD.enrolledInAssigned);
dataRD.enrolledInAssignedCOA(dataRD.assignedToPref==0)=nan;
dataRD.placedInAnyPreferenceNWIE=dataRD.placedInAnyPreferenceNoWaitlistIniEnd;
dataRD.placedInAnyPreferenceNoRiskIE=dataRD.placedInAnyPreferenceNoRiskIniEnd;

%% Extra variables

% 2020 survey data

% From "Mineduc/encuestas/riesgo2020/doFiles/readCleanData.do"
survey=readtable([dirData,'2020/dataEncuestaMail.csv']);


survey.withSurvey=true(height(survey),1);
survey.anho=scalarForTable(2020,survey);
dataRD=outerjoin(dataRD,survey,'keys',{'id_postulante','anho'},'mergeKeys',true,'type','left');


% Merge declared risk (positive and negative version):
dataRD.declaredRisk=dataRD.probNoPlaced;
dataRD.declaredRisk(not(isnan(dataRD.probPlacedAny)))=1-dataRD.probPlacedAny(not(isnan(dataRD.probPlacedAny)));

dataRD.optimismBiasAny=(1-dataRD.declaredRisk)-(1-dataRD.riskExpostEnd);
dataRD.optimismBias1st=dataRD.probPlaced1st-(1-dataRD.riskExpost_1stPrefEnd);
dataRD.trueProbPlaced1st=(1-dataRD.riskExpost_1stPrefEnd);


dataRD.completed=double(dataRD.progress==100);
dataRD.answerExpectationsQs=not(isnan(dataRD.declaredRisk));

%dataRD.completed(not(dataRD.withSurvey))=nan; 
dataRD.DRiskExpostIniEndSurvey=dataRD.DRiskExpostIniEnd;
dataRD.DRiskExpostIniEndSurvey(not(dataRD.withSurvey))=nan; 
dataRD.addAnyIniEndSurvey=double(dataRD.addAnyIniEnd);
dataRD.addAnyIniEndSurvey(not(dataRD.withSurvey))=nan; 
dataRD.riskExpostEndSurvey=dataRD.riskExpostEnd;
dataRD.riskExpostEndSurvey(not(dataRD.answerExpectationsQs))=nan; 
dataRD.riskExpostIniSurvey=dataRD.riskExpostIni;
dataRD.riskExpostIniSurvey(not(dataRD.answerExpectationsQs))=nan; 

dataRD.sureWillGetIn=double(dataRD.declaredRisk==0);
dataRD.sureWillGetIn(isnan(dataRD.declaredRisk))=nan; 

changeToNum={'addAnyIfMoreKnowledge','probIncreaseIfAdd','receivedRecommendationAddMore'};
for c=1:length(changeToNum)
    
    varCh=changeToNum{c};
    varChNew=[changeToNum{c},'Num'];
    
    dataRD.(varChNew)=scalarForTable(nan,dataRD);
    dataRD.(varCh)=categorical(dataRD.(varCh));
    dataRD.(varChNew)(dataRD.(varCh)=='Yes')=1;
    dataRD.(varChNew)(dataRD.(varCh)=='No')=0;
    assert(mean(dataRD.(varChNew),'omitnan')>0&&mean(dataRD.(varChNew),'omitnan')<1)
end


%%
%notIn2020=[notIn2020,'enrolledInAssignedCOA'];

% 5th column defines if IV is calculated or not.
outcomes={...
    'withSurvey','Survey take-up','stake','A. Survey takeup and completion',0;...
    'answerExpectationsQs','Answered expectation questions','answE','',0;...
     'addAnyIniEndSurvey','Add any','aaInSurv','B. Application behavior in survey sample',0;...
      'DRiskExpostIniEndSurvey','$\Delta$ Risk','drEPinSurv','',0;...
    'declaredRisk','Subjective P(not assigned to any)','spAny','C. Subjective beliefs',0;...
   % 'sureWillGetIn','Zero subjective risk','zsr','',0;...
    'probPlaced1st','Subjective P(assigned to 1st)','sp1st','',0;...
    %'riskExpostEndSurvey','[Survey pop.] True P(not assign in any)','rEES','',0;...
    %'riskExpostEnd','[All] True P(not assign in any)','rEE','',0;...
    %'trueProbPlaced1st','True P(assign in 1st)','tp1st','',0;...
    %'optimismBiasAny','Optimism bias on P(assigned to any)','obAny','',0;...
    %'optimismBias1st','Optimism bias on P(assigned to 1st)','ob1st','',0;...
    %'receivedRecommendationAddMoreNum','Got recommendation about adding more schools?','rw','',0;...
    %'addAnyIfMoreKnowledgeNum','Would add an unknown school if more search?','aaik','',0;...
    %'probIncreaseIfAddNum','Higer chances of placement if add?','pi','',0;...
    'satisf_1','Satisfaction if assigned to 1st choice (1--7)','s1','D. Stated preferences',0;...
    %'satisf_last','Satisfaction Last opt','sl','',0;...
    %'satisf_noPlace','Satisfaction no placement','snp','',0;...
    
    };



% outcomes={...
%     'riskExpostEndSurvey','[Survey pop.] True P(not assign in any) LAST ATTEMPT','rEES','',0;...
%     'riskExpostEnd','[All] True P(not assign in any) LAST ATTEMPT','rEE','',0;...
%     'riskExpostIniSurvey','[Survey pop.] True P(not assign in any) FIRST ATTEMPT','rEIS','',0;...
%     'riskExpostIni','[All] True P(not assign in any) FIRST ATTEMPT','rEI','',0;...
%     };


% Beliefs of risky that did not react (are those higher than

% Avoid calcualte those for year<2020
surveyOutcomes={};

if(anho==2020)
    outcomes=outcomes(not(ismember(outcomes(:,1),notIn2020)),:);
end



dataRD.Properties.VariableDescriptions{'riskPopup'}='Predicted placement risk';
for o=1:size(outcomes,1)
    
    dataRD.Properties.VariableDescriptions{outcomes{o,1}}=outcomes{o,2};
end


assert(allunique(outcomes(:,1)))
assert(allunique(outcomes(:,2)))
assert(allunique(outcomes(:,3)))

% Sort dataRD as it was after merging new vars
dataRD=sortrows(dataRD,'orden');
data=dataRD(:,[{'riskPopup'},outcomes(:,1)']);
inAnyPob=false(height(data),1);




%% Generate stata commands
commands=cell(size(subpobs,1)*size(outcomes,1),2);
counter=1;

for p=1:size(subpobs,1)
    
    data.(sprintf('subpop%i',p))=subpobs{p,2};
    inAnyPob=inAnyPob|subpobs{p,2};
    
    for o=1:size(outcomes,1)
        
        % Avoid computing outcomes that are not available:
        noCalcular=(strcmp(subpobs{p,3},'2020')|strcmp(subpobs{p,3},'2020comp'))&ismember(outcomes{o,1},notIn2020);
        
        noCalcular=noCalcular|not(strcmp(subpobs{p,3},'2020'))&ismember(outcomes{o,1},surveyOutcomes);
        
        
        
        if(not(noCalcular))
            
            
            % Full bandwidth
            if(tableBandwidthComparison||strcmp(bandwidthTable,'full')||plotRDs)
                commands(counter,:)={sprintf('%s_%i_full',outcomes{o,3},p),sprintf('rdrobust %s riskPopup if subpop%i==1, c(.3) p(2) h(.28 .68)',outcomes{o,1},p)};counter=counter+1;
            end
            % Full loccal fixed bandwidth
            if(tableBandwidthComparison||strcmp(bandwidthTable,'local'))
                commands(counter,:)={sprintf('%s_%i_local',outcomes{o,3},p),sprintf('rdrobust %s riskPopup if subpop%i==1, c(.3)  h(.1 .1)',outcomes{o,1},p)};counter=counter+1;
            end
            % Full local auto bandwidht
            if(tableBandwidthComparison||strcmp(bandwidthTable,'localAuto'))
                commands(counter,:)={sprintf('%s_%i_localAuto',outcomes{o,3},p),sprintf('rdrobust %s riskPopup if subpop%i==1, c(.3) ',outcomes{o,1},p)};counter=counter+1;
            end
            
            % If with IV:
            if(outcomes{o,5}==1&&~isempty(subpobs{p,4}))
                
                endogenousVar=subpobs{p,4};
                
                % Full bandwidth
                if(strcmp(bandwidthTable,'full'))
                    commands(counter,:)={sprintf('%s_%i_full_iv',outcomes{o,3},p),sprintf('rdrobust %s riskPopup if subpop%i==1, fuzzy(%s) c(.3) p(2) h(.28 .68)',outcomes{o,1},p,endogenousVar)};counter=counter+1;
                end
                % Full loccal fixed bandwidth
                if(strcmp(bandwidthTable,'local'))
                    commands(counter,:)={sprintf('%s_%i_local_iv',outcomes{o,3},p),sprintf('rdrobust %s riskPopup if subpop%i==1, fuzzy(%s) c(.3)  h(.1 .1)',outcomes{o,1},p,endogenousVar)};counter=counter+1;
                end
                % Full local auto bandwidht
                if(strcmp(bandwidthTable,'localAuto'))
                    commands(counter,:)={sprintf('%s_%i_localAuto_iv',outcomes{o,3},p),sprintf('rdrobust %s riskPopup if subpop%i==1, fuzzy(%s) c(.3) ',outcomes{o,1},p,endogenousVar)};counter=counter+1;
                end
            end
        end
    end
end
commands=commands(not(cellfun(@isempty,commands(:,1),'UniformOutput',true)),:);


if(false)
    % Run specific rd
    dataRD.auxRest=dataRD.restrAll&dataRD.mandatoryToAdd==0;
    a=stataCommand('rdrobust lengthEnd riskPopup if auxRest==1, c(.3) p(1) h(.1 .1)',dataRD)
    stataExpress('rdrobust  DRiskExpostIniEnd riskPopup if auxRest==1, fuzzy(addAnyIniEnd) c(.3) p(1) h(.1 .1)',data)
end

% Run Stata:
data=data(inAnyPob,:);
res=stataCommand(commands,data);


%% Make plots and table
close all

cantPobs=(size(subpobs,1));
cantRegs=(size(outcomes,1));

matrix_nl=nan(cantRegs,cantPobs);
matrix_nr=nan(cantRegs,cantPobs);
matrix_b=nan(cantRegs,cantPobs);
matrix_se=nan(cantRegs,cantPobs);

matrix_b_iv=nan(cantRegs,cantPobs);
matrix_se_iv=nan(cantRegs,cantPobs);
matrix_nl_iv=nan(cantRegs,cantPobs);
matrix_nr_iv=nan(cantRegs,cantPobs);

meanVars=nan(cantRegs,1);

matrix_b_l=nan(cantRegs,cantPobs);
matrix_se_l=nan(cantRegs,cantPobs);

matrix_b_r=nan(cantRegs,cantPobs);
matrix_se_r=nan(cantRegs,cantPobs);

matrix_biasCorr_ci=nan(cantRegs,cantPobs,2);



regNames=fieldnames(res);

cellsubfig=cell(maxHorzPlots,ceil(cantRegs/maxHorzPlots)); % Defined transposed to fill it with scalar indexing.
counter=1;

for p=1:cantPobs
    for r=1:cantRegs
        
        if(posAll==p)
            meanVars(r,1)=mean(dataRD.(outcomes{r,1}),'omitnan');
            
        end
        
        regName_i=sprintf('%s_%i_%s',outcomes{r,3},p,bandwidthTable);
        if(ismember(regName_i,regNames))
            res_i=res.(regName_i);
            
            % Beta
            matrix_b(r,p)=res_i.tau_cl;
            % Sd Beta
            matrix_se(r,p)=res_i.se_tau_cl;
            
            % Beta left
            matrix_b_l(r,p)=res_i.beta_p_l(1);
            % Sd Beta left
            matrix_se_l(r,p)=sqrt(res_i.V_cl_l(1));
            
            % Beta right
            matrix_b_r(r,p)=res_i.beta_p_r(1);
            % Sd Beta right
            matrix_se_r(r,p)=sqrt(res_i.V_cl_r(1));
            
            % Bias corrected Confidence interval (alpha=5%)
            matrix_biasCorr_ci(r,p,1)=res_i.tau_bc-norminv(.975)*res_i.se_tau_rb;
            matrix_biasCorr_ci(r,p,2)=res_i.tau_bc+norminv(.975)*res_i.se_tau_rb;
            
            % N
            matrix_nl(r,p)=res_i.N_h_l;
            matrix_nr(r,p)=res_i.N_h_r;
            
            % If with IV:
            if(outcomes{r,5}==1&&~isempty(subpobs{p,4}))
                regName_i_iv=sprintf('%s_%i_%s_iv',outcomes{r,3},p,bandwidthTable);
                
                res_i_iv=res.(regName_i_iv);
                % Beta
                matrix_b_iv(r,p)=res_i_iv.tau_cl;
                % Sd Beta
                matrix_se_iv(r,p)=res_i_iv.se_tau_cl;
                
                % N
                matrix_nl_iv(r,p)=res_i_iv.N_h_l;
                matrix_nr_iv(r,p)=res_i_iv.N_h_r;
                
            end
            
            if(plotRDs)
                
                
                res_i=res.(sprintf('%s_%i_%s',outcomes{r,3},p,'full'));
                res_i_table=res.(sprintf('%s_%i_%s',outcomes{r,3},p,bandwidthTable));
                
                plotsub=subpobs{p,2};
                
                figure
                plotRD(res_i,dataRD,'subpop',plotsub,'otherPointEstimate',res_i_table,'plotQuantiles',plotQuantiles,'quantiles',quantiles);
                
                if(titlePlotRD)
                    title(sprintf('RD %s - %s',outcomes{r,2},subpobs{p,1}))
                    
                end
                
                if(savePlotRDs)
                    % Guardo pal latexSubfigure
                    filePlot=sprintf(sprintf('%s_P_%s_%s.png',anhoStr,outcomes{r,3},subpobs{p,3}));
                    cellsubfig{counter}=  easyExport([dirPlots,filePlot],'title',filePlot);
                    counter=counter+1;
                end
                
                
                
                if(plotWithZoom)
                    % Plot with zoom
                    figure
                    
                    plotRD(res_i,dataRD,'subpop',plotsub,'newxlim',[.1,.5],'nb',100,'otherPointEstimate',res_i_table,'plotQuantiles',plotQuantiles,'quantiles',quantiles);
                    easyExport([dirPlots,sprintf(sprintf('%s_P_%s_%s_withZoom.png',anhoStr,outcomes{r,3},subpobs{p,3}))]);
                end
                
                
                
            else
                fprintf('Ojo: reg %s no existe para subpob %s\n',regName_i,subpobs{p,1});
            end
        end
    end
    
    %vector_Nl(1,p)=sum(dataRD.riskPopup<.3&subpobs{p,2});
    %vector_Nr(1,p)=sum(dataRD.riskPopup>.3&subpobs{p,2});
    
end





%% RD main table
matTable=nan(size(matrix_b).*[1 2]+[2 0]);
matTable_sd=nan(size(matrix_b).*[1 2]+[2 0]);

matTable(:,1:2:end)=[matrix_b;...
    matrix_nl(end,:)...
    ;matrix_nr(end,:)];

if(false)
matTable(:,2:2:end)=[matrix_b_iv;...
    matrix_nl_iv(find(cell2mat(outcomes(:,5)),1,'last'),:)...
    ;matrix_nr_iv(find(cell2mat(outcomes(:,5)),1,'last'),:)];
end

matTable_sd(:,1:2:end)=[matrix_se;nan(2,size(matrix_se,2))];
if(false)
matTable_sd(:,2:2:end)=[matrix_se_iv;nan(2,size(matrix_se_iv,2))];
end

withInfo=not(all(isnan(matTable),1));


header=repmat({''},2,size(matTable,2));
header(1,1:2:end)=subpobs(:,1)';
header(1,2:2:end)=subpobs(:,1)';
header(2,2:2:end)={'IV'};

matTable=matTable(:,withInfo);
matTable_sd=matTable_sd(:,withInfo);
header=header(:,withInfo);
header=header(any(not(cellfun(@isempty,header)),2),:);

% add mean
matTable=[matTable,[matrix_b_l;nan;nan]];
matTable_sd=[matTable_sd,nan(length(matTable_sd),1)];

opt=struct;
opt.header=[header,{'$E[Y|X=0.3^{-}]$'}];
opt.stderrs=mat2cellstr(matTable_sd,'conParentesis',true,'precisionDecimal','%.3f');
opt.primeracolumna=[outcomes(:,2);{'NL';'NR'}];
opt.addColumnNumber=true;
if(anho==0)
    opt.columnafantasma=4;
    
else
    %opt.columnafantasma=[4];
    opt.title=sprintf('%s Sample - Pop-Up effect',anhoStr);
end
opt.positionParameter='H';
opt.filaFantasma=[size(matTable,1)-2];
opt.columnaFantasma=[1 2];
paneles=find(not(ismissing(outcomes(:,end-1))));
opt.panel=[num2cell(paneles-1),outcomes(paneles,end-1)];
%opt.alignmentFirstCol={'L{3cm}'};
opt.adjust=true;

opt.file=sprintf('%s%s_tablePopup_surveyOutcomes',dirTable,anhoStr);

opt.title='RD Estimates of Platform Pop-Up Effects on Subjective Beliefs';
opt.label='tab:RDpopup-survey';

opt.note='Local linear RD estimates of platform pop-up effects on survey reported subjective beliefs. Computed using triangular kernel with bandwidth 0.1. Heteroskedasticity-robust nearest neighbor variance estimator with minimum of 3 neighbors reported in parentheses; computed as in \citet{calonico2014robust}. The second column reports the below-cutoff means of the row variables. Panels  B and C restrict the sample to applicants who  completed the beliefs module of the 2020 survey. See section \ref{sec:subbeliefsRD} for details.';

tabla=cell2latex(mat2cellstr(matTable,'precisionDecimal','%.3f','revisarFilas',true),'opts',opt);
if(compileLatexTable)
compileLatex(tabla)
end

%%

if(plotRDs&&savePlotRDs)
    close all
    numOfPages=ceil(size(cellsubfig,2)/maxVertPlots);
    pages=cell(numOfPages,1);
    for p=1:numOfPages
        
        newEnd=min((p*maxVertPlots),size(cellsubfig,2));
        note='';
        %note='Pop-up warning RD effect fits and point estimates by bandwidth for outcomes listed in panel titles. ``Full'''': global quadratic. ``+/- 0.1'''': local linear within 0.1 bandwidth. ``rdbwselect'''': optimal bandwidth selection using \citet{calonico2014robust}.';
        if p==1
            label='fig:appBW';
        else
            label=sprintf('fig:appBW_%i',p);
        end
        label='';
        
        %pages{p}=subfiguresLatex(cellsubfig(:,((p-1)*maxVertPlots+1):newEnd)','erp','','erpf', '','caption','RD plots',...
         %   'scale',.45,'docwidth',1.2,'note',note,'label',label);
        
    end
    fileSubfigure='subfigRDsSurvey';
    if(compileLatexTable)
    %compileLatex(horzcat(tabla,newline,'\clearpage',newline,pages{:}),'dirTex',dirPlots,'texFile',fileSubfigure);
    end
    %% Append with table of results:
    %append_pdfs([dirPlots,'surveyOutcomes.pdf'],{[dirPlots,fileSubfigure],''})
    
end

%% Make table of bandwidth comparison

if(tableBandwidthComparison)
    cantRobuts=3;
    labelRobust={'Full','+-0.1','rdbwselect'};
    cantRegs=(size(outcomes,1));
    
    matrix_nl=nan(cantRegs,cantRobuts);
    matrix_nr=nan(cantRegs,cantRobuts);
    matrix_b=nan(cantRegs,cantRobuts);
    matrix_se=nan(cantRegs,cantRobuts);
    
    matrix_bl=nan(cantRegs,cantRobuts);
    matrix_br=nan(cantRegs,cantRobuts);
    
    regNames=fieldnames(res);
    
    
    cellsubfig=cell(maxHorzPlots,ceil(cantRegs/maxHorzPlots)); % Defined transposed to fill it with scalar indexing.
    counter=1;
    for p=posBandwidth
        for r=1:cantRegs
            
            regName_i=sprintf('%s_%i_full',outcomes{r,3},p);
            regName_i_local=sprintf('%s_%i_local',outcomes{r,3},p);
            regName_i_localAuto=sprintf('%s_%i_localAuto',outcomes{r,3},p);
            
            if(ismember(regName_i_local,regNames))
                res_i=cell(cantRobuts,1);
                
                res_i{1}=res.(regName_i);
                res_i{2}=res.(regName_i_local);
                res_i{3}=res.(regName_i_localAuto); % AUTO tiene que ser la ultima!
                
                
                for i=1:cantRobuts
                    % Beta
                    matrix_b(r,i)=res_i{i}.tau_cl;
                    % Sd Beta
                    matrix_se(r,i)=res_i{i}.se_tau_cl;
                    
                    % N
                    matrix_nl(r,i)=res_i{i}.N_h_l;
                    matrix_nr(r,i)=res_i{i}.N_h_r;
                    
                    % N
                    matrix_bl(r,i)=res_i{i}.h_l;
                    matrix_br(r,i)=res_i{i}.h_r;
                    
                end
                
                if(plotAndSaveRobust)
                    
                    plotsub=subpobs{p,2};
                    
                    
                    
                    % Plot alternatives bandwidths:
                     linestylesAB={'-','--','-.'};
                    colors=linspecer(3);
                    c1=colors(1,:);
                    c2=colors(2,:);
                    c3=colors(3,:);
                    
                    figure
                    % Plot full bandwidth:
                    
                    a1=plotRD(res_i{1},dataRD,'subpop',plotsub,'color',c1,'linestyle',linestylesAB{1} ); hold on;
                    if(a1.posBeta>.5);shift=-.1;else;shift=.1;end
                    a2=plotRD(res_i{2},dataRD,'subpop',plotsub,'withCutoffVerticalLine',0,'withBinsreg',0,'posbeta',a1.posBeta+shift,'color',c2,'linestyle',linestylesAB{2} );  hold on;
                    a3=plotRD(res_i{3},dataRD,'subpop',plotsub,'withCutoffVerticalLine',0,'withBinsreg',0,'posbeta',a1.posBeta+2*shift,'color',c3,'linestyle',linestylesAB{3} );
                    
                    legend([a1.functionLine,a2.functionLine,a3.functionLine],labelRobust,'interpreter','latex')
                    
                    hold off
                    
                    % Guardo pal latexSubfigure
                    cellsubfig{counter}=easyExport([dirPlotsR,sprintf(sprintf('%s_P_%s_%s_bandComparison%s.png',anhoStr,outcomes{r,3},subpobs{p,3}))],'caption',outcomes{r,2});
                    counter=counter+1;
                    
                    
                    close
                    
                    %                 % Plot with zoom
                    %                 figure
                    %                 a=plotRD(res_i,dataRD,plotsub,'newxlim',[.1,.5]);
                    %                 easyExport([dirPlots,sprintf(sprintf('%s_P_%s_%s_withZoom.png',anhoStr,outcomes{r,3},subpobs{p,3}))]);
                    %
                    %                 close
                    
                end
            end
        end
        
        
    end
    
   %%
    if(plotAndSaveRobust)
    for p=1:ceil(size(cellsubfig,2)/maxVertPlots)
        
        newEnd=min((p*maxVertPlots),size(cellsubfig,2));
        note='Pop-up warning RD effect fits and point estimates by bandwidth for outcomes listed in panel titles. ``Full'''': global quadratic. ``+/- 0.1'''': local linear within 0.1 bandwidth. ``rdbwselect'''': optimal bandwidth selection using \citet{calonico2014robust}. See  section \ref{sec:RD} for details.';
        if p==1
            label='fig:appBW-survey';
        else
            label=sprintf('fig:appBW-survey-%i',p);
        end
        %subfiguresLatex(cellsubfig(:,((p-1)*maxVertPlots+1):newEnd)','file',[dirPlotsR,'containers/subfigRobustRDs-survey_',num2str(p)],'erp','figuresCL/RDs_multBandwidth/','erpf', 'figuresCL/RDs_multBandwidth/','caption','Multiple bandwidths RD plots of platform-based pop-up warning effects (outcomes in Table \ref{tab:RDpopup-survey})',...
         %   'scale',.52,'docwidth',1.2,'note',note,'label',label);
    end
    end
    %%
    
    cellTable=[[mat2cellstr(matrix_b,'precisionDecimal','%.3f','revisarCols',true),mat2cellstr([matrix_bl(:,end),matrix_br(:,end),matrix_nl(:,end),matrix_nr(:,end)],'precisionDecimal','%.2f','revisarCols',true)];...
        mat2cellstr([[matrix_nl(end,1:2);matrix_nr(end,1:2)],nan(2,5)])];
    
    nans=nan(size(cellTable));
    nans(1:size(matrix_se,1),1:size(matrix_se,2))=matrix_se;
    matTable_sd=nans;
    
    
    opt=struct;
    opt.header=[[{'Bandwidth'},labelRobust,{'rdbwselect','rdbwselect','rdbwselect','rdbwselect'}];...
        {'','Estimate','Estimate','Estimate','BW left','BW right','N left','N right'}];
    opt.stderrs=mat2cellstr(matTable_sd,'conParentesis',true,'precisionDecimal','%.3f');
    opt.primeracolumna=[outcomes(:,2);{'N left';'N right'}];
    %opt.stars=getStars(matTable,matTable_sd);
    opt.addColumnNumber=true;
    opt.columnafantasma=[1 2];
    opt.title='Platform Pop-Up RD Estimates of Enrolled School Outcomes (Table \ref{tab:RDpopup-survey}) with Alternate Bandwidths';
    opt.filaFantasma=size(outcomes,1);
    opts.positionParameter='H';
    paneles=find(not(ismissing(outcomes(:,4))));
    opt.panel=[num2cell(paneles-1),outcomes(paneles,4)];
    %opt.alignmentFirstCol={'L{3cm}'};
    opt.adjust=true;
    opt.label='tab:popupBW-survey';
    opt.file=sprintf('%s%s_tablePopupBandwidth-survey',dirTable,anhoStr);
    opt.note=['Local linear and full sample quadratic polynomial RD estimates of pop-up effects from warning pop-up on application platform. Computed using triangular kernel with different bandwidths. ``Full'''' bandwidth uses 2nd order polynomial fit, while "+-0.1" and rdbwselect uses 1st order (local) polynomial. Heteroskedasticity-robust nearest neighbor variance estimator with minimum of 3 neighbors reported in parentheses; computed as in \citet{calonico2014robust}. '];
    tablaComp=cell2latex(cellTable,'opts',opt);
    if(compileLatexTable)
    compileLatex(tablaComp)
    end
end